Emotion prediction device, method, program, and vehicle

ABSTRACT

An emotion prediction device mounted on a vehicle includes a control unit. The control unit is capable of executing functions of a first prediction unit configured to predict an emotion of a user based on a facial expression of the user, a second prediction unit configured to predict an emotion of the user based on the motion of the user, and a third prediction unit configured to predict the emotion of the user based on the emotion predicted by the first prediction unit and the emotion predicted by the second prediction unit. The control unit is capable of restricting execution of at least a part of the functions of the first, second, and third prediction units, based on at least a processing load of the control unit.

CROSS-REFERENCE TO RELATED APPLICATION

This application claims priority to Japanese Patent Application No.2020-017904 filed on Feb. 5, 2020, incorporated herein by reference inits entirety.

BACKGROUND 1. Technical Field

The present disclosure relates to a device that predicts an emotion of auser.

2. Description of Related Art

Various technologies have been proposed for capturing an image of aperson's face and body, extracting facial expressions, body postures,and motions, and predicting emotions based on those pieces of data. Forexample, Japanese Unexamined Patent Application Publication No.2008-146318 discloses a technology in which a facial expression map iscreated by learning, in advance, a correlation between facialexpressions and emotions, and emotions are predicted from facialexpressions of a person based on the facial expression map.

SUMMARY

Such an emotion prediction function may be implemented by an electronicdevice, i.e., an electronic control unit (ECU) mounted on a vehicle thatpredicts emotions of a user aboard the vehicle, thereby being capable ofproviding various services according to the emotions of the user.

Generally, the emotion prediction process has a high processing load.Therefore, in a case where the ECU that executes the emotion predictionalso executes processes of other applications, the load of the emotionprediction process may delay the processes of other applications, or aprocessor may become overheated such that the processes are forciblyinterrupted.

The present disclosure addresses such a shortcoming by providing anemotion prediction device capable of executing an emotion predictionprocess while reducing influence on other applications.

An emotion prediction device according to one aspect of the presentdisclosure is mounted on a vehicle and includes a control unit. Thecontrol unit is configured to execute functions of a first predictionunit configured to predict an emotion of a user based on a facialexpression of the user, a second prediction unit configured to predictan emotion of the user based on a motion of the user, and a thirdprediction unit configured to predict an emotion of the user based onthe emotion predicted by the first prediction unit and the emotionpredicted by the second prediction unit. The control unit is configuredto restrict execution of at least a part of the functions of the firstprediction unit, the second prediction unit, and the third predictionunit, based on at least a processing load of the control unit.

According to the present disclosure, an emotion prediction devicerestricts the execution of an emotion prediction process based on theprocessing load of a control unit. Therefore, it is possible to executethe emotion prediction process while reducing the influence on otherapplications.

BRIEF DESCRIPTION OF THE DRAWINGS

Features, advantages, and technical and industrial significance ofexemplary embodiments of the disclosure will be described below withreference to the accompanying drawings, in which like signs denote likeelements, and wherein:

FIG. 1 is a diagram illustrating functional blocks of an emotionprediction device and its peripheral portion according to an embodiment;

FIG. 2 is a flowchart illustrating a state shift in an emotionprediction process of the emotion prediction device according to theembodiment; and

FIG. 3 is a diagram illustrating an effect of reducing a processing loadof the emotion prediction device according to the embodiment.

DETAILED DESCRIPTION OF EMBODIMENTS Embodiments

An emotion prediction device according to an embodiment of the presentdisclosure restricts an emotion prediction process so as to reduce aload of the emotion prediction process when it is predicted that theemotion prediction process cannot be suitably executed, such as a casewhere a processing load of a central processing unit (CPU) or a graphicsprocessing unit (GPU), included in a control unit, is high, a case wherea facial expression of a user cannot be acquired, or a case where it ispredicted that motions of the user do not reflect his/her emotions.Accordingly, it is possible to prevent processes of other applicationsexecuted by the emotion prediction device from being delayed or stopped.Hereinafter, the present embodiment will be described in detail withreference to drawings.

Configuration

FIG. 1 illustrates functional blocks of an emotion prediction device 100according to the present embodiment. The emotion prediction device 100includes a first prediction unit 11, a second prediction unit 12, athird prediction unit 13, and a load acquisition unit 14. The functionsof these units are executed by a control unit 10. The control unit 10 isa computer typically including a CPU, a GPU, and a memory.

The first prediction unit 11 executes emotion prediction based on afacial expression in which it periodically acquires a captured image ofthe face of the user, extracts facial expressions, and predicts emotionsbased on the facial expressions.

The second prediction unit 12 executes emotion prediction based onmotion in which it periodically acquires a captured image of the body ofthe user, extracts the positions and movements of the hands, head,torso, and the like, further extracts the motions specified by thepositions and movements thereof, and predicts the user's emotions basedon the motions.

The third prediction unit 13 executes comprehensive emotion predictionin which it acquires the facial emotion prediction result by the firstprediction unit 11 and the motional emotion prediction result by thesecond prediction unit 12, and comprehensively predicts the emotionsbased on these results. In the comprehensive emotion prediction, theaccuracy of the emotion prediction can be further enhanced by combiningthe facial emotion with the motional emotion, as compared to a casewhere only one of the facial emotion prediction and the motional emotionprediction is executed.

The emotion prediction method executed by each of the first predictionunit 11, the second prediction unit 12, and the third prediction unit 13is not limited, and various algorithms can be appropriately selected. Asa combination of states in which the emotion prediction processes of theprediction units are executed, the following five states can beexemplified.

A first state is a state in which the processes of the first predictionunit 11, the second prediction unit 12, and the third prediction unit 13are all executed. In this state, the comprehensive emotion predictionresult by the third prediction unit 13 is adopted as the emotionprediction result by the emotion prediction device 100. This state is anormal state of the emotion prediction device 100.

A second state is a state where the process of the first prediction unit11 is executed while the processes of the second prediction unit 12 andthe third prediction unit 13 are stopped. In this state, the facialemotion prediction result by the first prediction unit 11 is adopted asthe emotion prediction result by the emotion prediction device 100.

A third state is a state where the process of the second prediction unit12 is executed while the processes of the first prediction unit 11 andthe third prediction unit 13 are stopped. In this state, the motionalemotion prediction result by the second prediction unit 12 is adopted asthe emotion prediction result by the emotion prediction device 100.

A fourth state is a state where the processes of the first predictionunit 11, the second prediction unit 12, and the third prediction unit 13are all executed, but the cycle of executing each process is longer thanin the first state described above. In this state, the comprehensiveemotion prediction result by the third prediction unit 13 is adopted asthe emotion prediction result by the emotion prediction device 100, asin the first state, but the emotion prediction process is executed lessfrequently than in the first state.

A fifth state is a state where the processes of the first predictionunit 11, the second prediction unit 12, and the third prediction unit 13are all stopped. In this state, the emotion prediction executed by theemotion prediction device 100 is stopped.

In the second, third, fourth, and fifth states, the processing loads onthe CPU and GPU are lower than those in the first state due to thestopping of the processes or the reduction in the execution frequency.

The load acquisition unit 14 acquires the respective processing loads ofthe CPU and GPU. For example, the usage rate can be used as an index ofthe processing load, but an appropriate index may be used according tothe configuration of the processor, such as a CPU or GPU, included inthe control unit 10, function sharing, and load features of the emotionprediction process.

The control unit 10 controls the state shift of shifting between thefirst and fifth states based on, for example, the processing loadacquired by the load acquisition unit 14 to be described below.

In addition to the emotion prediction process described above, thecontrol unit 10 can execute one or more applications in parallel withthe emotion prediction process to implement various functions of avehicle.

The emotion prediction device 100 is installed in the vehicle and isconnected to various devices provided in the vehicle so as to enablecommunication therebetween via an in-vehicle network. Examples of thevarious devices include a time-of-flight (TOF) camera 21, an infrared(IR) camera 22, a directional microphone 23, a shift control unit 24, anoperation acceptance unit 25, a courtesy switch 26, a seating sensor 27,and a navigation device 28.

The TOF camera 21 and the IR camera 22 are distance image sensorscapable of measuring a distance to a subject, and are arranged such thatimages of the user's body and face in a normal traveling posture can becaptured. For example, the first prediction unit 11 may execute thefacial emotion prediction process based on the image of the user's facecaptured by the IR camera 22. In another example, the second predictionunit 12 may execute the motional emotion prediction process based on theimage of the user's upper body captured by the TOF camera 21.

The directional microphone 23 is a microphone capable of collecting avoice of the user and specifying, for example, in which direction theuser is speaking in a vehicle cabin according to directionalcharacteristics.

The shift control unit 24 is a device that detects and controls gearpositions such as P (parking), D (drive), and R (reverse).

The operation acceptance unit 25 is a device that includes buttons,switches, a touchscreen, and the like, and accepts various operationsfrom the user.

The courtesy switch 26 is a sensor that detects opening/closing of avehicle door.

The seating sensor 27 is a sensor that detects whether the user isseated on a seat.

The navigation device 28 is a device that guides the user along a routeand provides information on an estimated arrival time to a destinationdesignated by the user, based on map information and locationinformation of the vehicle acquired by a GPS.

Additionally, a device that acquires the emotion prediction result bythe emotion prediction device 100 and uses the result for processing isconnected to the in-vehicle network. A device having an agent functionfor executing a conversation function with the user can be exemplifiedas such a device.

Process

FIG. 2 is a flowchart illustrating a state shift process executed by thecontrol unit 10 of the emotion prediction device 100. The state shift ofshifting between the first and fifth states will be described withreference to FIG. 2. The normal state will be described as a startingpoint. Further, it is assumed that the emotion prediction device 100predicts the emotion of the user seated in a driver's seat.

Step S101: The control unit 10 sets the execution state of the emotionprediction process to the normal state (first state) in which theprocesses of the first prediction unit 11, the second prediction unit12, and the third prediction unit 13 are all executed.

Step S102: The control unit 10 determines whether the user is seated inthe driver's seat. For example, the control unit 10 can make thedetermination based on the detection result of the seating sensor 27. Ina case where it is determined that the user is seated in the driver'sseat, the process proceeds to step S103, and in a case where it isdetermined that that the user is not seated, the process proceeds tostep S107. Moreover, the detection result of the seating sensor 27 iscombined with the detection of the opening/closing of a door of thedriver's seat by the courtesy switch 26. For example, it is determinedthat the user is seated in the driver's seat in a case where thecourtesy switch 26 detects the opening of the door, the seating sensor27 detects the fact that the user is seated, and then the courtesyswitch 26 further detects the closing of the door. Accordingly, whetherthe user is seated can be determined more accurately.

Step S103: The control unit 10 determines whether the GPU has a highload based on the processing load of the GPU acquired from the loadacquisition unit 14. For example, the control unit 10 can determine thatthe load is high when the processing load of the GPU is equal to orhigher than a first value, and can determine that the load is not highwhen the processing load thereof is lower than the first value. In acase where it is determined that the GPU has a high load, the processproceeds to step S104, and otherwise, the process proceeds to step S114.

Step S104: The control unit 10 determines whether the facial expressionof the user can be acquired. For example, when it is detected that theuser's voice acquired from the directional microphone 23 is directedfrom the driver's seat toward a rear seat, the control unit 10 candetermine that the user is facing toward the rear seat to have aconversation with the passenger seated in the rear seat, thus an imageof the user's face cannot be captured and the facial expression cannotbe acquired. Alternatively, for example, when it is detected that theshift position acquired from the shift control unit 24 is “R”, whichindicates that the vehicle is traveling in reverse, the control unit 10can determine that the user's face is facing the rear side so as tocheck the rear side, thus an image of the user's face cannot be capturedand the facial expression cannot be acquired. Further, in a case wherethe current state does not correspond to any of the states defined as astate where an image of the face cannot be captured, it can bedetermined that the user's facial expression can be acquired. In a casewhere it is determined that the user's facial expression can beacquired, the process proceeds to step S110, and in a case where it isdetermined that that the user's facial expression cannot be acquired,the process proceeds to step S105.

Step S105: The control unit 10 sets the execution state of the emotionprediction process to the state (third state) in which the process ofthe second prediction unit 12 is executed while the processes of thefirst prediction unit 11 and the third prediction unit 13 are stopped.

Step S106: The control unit 10 determines whether the GPU has a highload based on the processing load of the GPU acquired from the loadacquisition unit 14. For example, the control unit 10 can determine thatthe load is high when the processing load of the GPU is equal to orhigher than a second value, and that the load is not high when theprocessing load thereof is lower than the second value. In a case whereit is determined that the GPU has a high load, the process proceeds tostep S107, and when it is determined that the GPU does not have a highload, the process proceeds to step S109.

Step S107: The control unit 10 sets the execution state of the emotionprediction process to the state (fifth state) in which the processes ofthe first prediction unit 11, the second prediction unit 12, and thethird prediction unit 13 are all stopped.

Step S108: The control unit 10 determines whether a condition underwhich the fifth state is to be returned to the first state is satisfied.The condition under which the fifth state is to be returned to the firststate is that the user is seated and the user's facial expression can beacquired. Whether the user is seated can be determined in the samemanner as in step S102. Further, whether the user's facial expressioncan be acquired can be determined in the same manner as in step S104. Ina case where the condition under which the fifth state is to be returnedto the first state is satisfied, the process proceeds to step S101, andwhen the condition thereunder is not satisfied, the process proceeds tostep S107.

Step S109: The control unit 10 determines whether a condition underwhich the third state is to be returned to the first state is satisfied.The condition under which the third state is to be returned to the firststate is that the user's facial expression can be acquired and the GPUdoes not have a high load. Whether the user's facial expression can beacquired can be determined in the same manner as in step S104. Further,if the processing load of the GPU acquired from the load acquisitionunit 14 is lower than a third value, it can be determined that the GPUdoes not have a high load. In a case where the condition under which thethird state is to be returned to the first state is satisfied, theprocess proceeds to step S101, and when the condition thereunder is notsatisfied, the process proceeds to step S105.

Step S110: The control unit 10 determines whether the user is operatingthe device. For example, upon acquiring, from the navigation device 28,the operation acceptance unit 25, or the like, information indicatingthat the user is operating a button provided on the navigation device 28or an instrument panel, the control unit 10 can determine that the useris operating the device. In a case where the user is operating thedevice, the motion by the user is for operating the device and isunlikely to reflect the user's emotion. In a case where it is determinedthat the user is operating the device, the process proceeds to stepS112, and when it is determined that the user is not operating thedevice, the process proceeds to step S111.

Step S111: The control unit 10 determines whether the GPU has a highload based on the processing load of the GPU acquired from the loadacquisition unit 14. For example, the control unit 10 can determine thatthe load is high when the processing load of the GPU is equal to orhigher than a fourth value, and can determine that the load is not highin a case where the processing load thereof is lower than the fourthvalue. In a case where it is determined that the GPU has a high load,the process proceeds to step S112, and when it is determined that theGPU does not have a high load, the process proceeds to step S101.

Step S112: The control unit 10 sets the execution state of the emotionprediction process to the state (second state) in which the process ofthe first prediction unit 11 is executed while the processes of thesecond prediction unit 12 and the third prediction unit 13 are stopped.

Step S113: The control unit 10 determines whether a condition underwhich the second state is to be returned to the first state issatisfied. The condition under which the second state is to be returnedto the first state is that the user is not operating the device and theGPU does not have a high load. Whether the user is operating the devicecan be determined in the same manner as in step S110. Further, if theprocessing load of the GPU acquired from the load acquisition unit 14 islower than a fifth value, it can be determined that the GPU does nothave a high load. In a case where the condition under which the secondstate is to be returned to the first state is satisfied, the processproceeds to step S101, and when the condition thereunder is notsatisfied, the process proceeds to step S112.

Step S114: The control unit 10 determines whether the CPU has a highload based on the processing load of the CPU acquired from the loadacquisition unit 14. For example, the control unit 10 can determine thatthe load is high when the processing load of the CPU is equal to orhigher than a sixth value, and can determine that the load is not highin a case where the processing load thereof is lower than the sixthvalue. In a case where it is determined that the CPU has a high load,the process proceeds to step S115, and when it is determined that theCPU does not have a high load, the process proceeds to step S104.

Step S115: The control unit 10 sets the execution state of the emotionprediction process to the state (fourth state) in which the processes ofthe first prediction unit 11, the second prediction unit 12, and thethird prediction unit 13 are all executed but the cycle of executingeach process is longer than in the first state.

Step S116: The control unit 10 determines whether a condition underwhich the fourth state is to be returned to the first state issatisfied. The condition under which the fourth state is to be returnedto the first state is that the GPU does not have a high load and the CPUdoes not have a high load. If the processing load of the GPU acquiredfrom the load acquisition unit 14 is lower than a seventh value, it canbe determined that the GPU does not have a high load. If the processingload of the CPU acquired from the load acquisition unit 14 is lower thanan eighth value, it can be determined that the CPU does not have a highload. In a case where the condition under which the fourth state is tobe returned to the first state is satisfied, the process proceeds tostep S101, and in a case where the condition thereunder is notsatisfied, the process proceeds to step S115.

With such a state shift process, the execution state of the emotionprediction process by the emotion prediction device 100 is switched.Consequently, in a case where the processing load of the CPU or GPU ishigh, or in a case where the emotion prediction process cannot beexecuted appropriately due to, for example, the posture of the user, theemotion prediction process is curbed to reduce the load, and thus it ispossible to prevent the processing of other applications executed by thecontrol unit 10 from being delayed or stopped. The first to eighthvalues used for determining the load, described above, may be differenttherefrom or the same as described.

Further, in step S109, in a case where the condition under which thethird state is to be returned to the first state and an additionalcondition to be described below is further satisfied, the control unit10 may proceed to step S101, and in a case where the conditionthereunder is not satisfied, the process proceeds to step S105. Further,in step S113, in a case where the condition under which the second stateis to be returned to the first state and the additional condition to bedescribed below is further satisfied, the control unit 10 may proceed tostep S101, and in a case where the condition thereunder is notsatisfied, the process proceeds to step S112. The additional conditionmay be satisfied when, for example, any one of the following conditionsis satisfied.

(1) The user's voice is detected by the directional microphone 23 oranother microphone provided in the vehicle.

(2) Music is played by an audio device provided in the vehicle.

(3) The seating sensor 27 detects one or both of passengers seated inthe rear seat and a passenger seated in the passenger seat.Alternatively, the courtesy switch 26 detects the opening/closing of apassenger seat door or a rear door.

(4) The vehicle has arrived at the destination set in the navigationdevice 28.

In a case where the additional condition is satisfied, it is highlylikely that the user's emotions have changed or are likely to change. Ifthe state is shifted to the first state when such an additionalcondition is satisfied, the emotion prediction process can be furthercurbed and the load can be reduced.

Further, in a case where the possibility that the user's emotion willnot change continues to be high, the control unit 10 may reduce theexecution frequency of the emotion prediction process. For example,while the predicted arrival time to the destination derived by thenavigation device 28 is delayed each time it is updated due to, forexample, traffic congestion, the possibility that the user's annoyancewill not change continues to be high. Thus the first state may bereplaced by the fourth state in the state shift process described above.

FIG. 3 is a diagram schematically illustrating an effect of reducing theprocessing load of the emotion prediction process according to thepresent embodiment. In FIG. 3, the load of the emotion predictionprocess in the present embodiment is represented by a solid line, andfor comparison, the load of a case where the state (first state) inwhich the processes of the first prediction unit 11, the secondprediction unit 12, and the third prediction unit 13 are all executed ismaintained is represented by a dotted line.

Periods T1 to T7 illustrated in FIG. 3 are periods in which the user isnot aboard the vehicle. In those periods, the processes of the firstprediction unit 11, the second prediction unit 12, and the thirdprediction unit 13 are all stopped (fifth state).

Further, periods T2, T4, and T6 are periods in which the user is seated,the user's facial expression can be acquired, the user is not operatingthe device, and the control unit 10 does not have a high load. In thoseperiods, the processes of the first prediction unit 11, the secondprediction unit 12, and the third prediction unit 13 are all executed(first state).

Further, periods T3 and T5 are periods in which the user's facialexpression cannot be acquired, the user is operating the device, or thecontrol unit 10 has a high load. In those periods, the processes of thefirst prediction unit 11, the second prediction unit 12, and the thirdprediction unit 13 are partially restricted (any one of the second tofourth states).

As illustrated in FIG. 3, in periods T1, T3, T5, and T7, the load on thecontrol unit 10 that executes the emotion prediction process is curbedas compared with a case where the state (first state) in which theprocesses of the first prediction unit 11, the second prediction unit12, and the third prediction unit 13 are all executed is maintained.

Advantageous Effects

In the embodiment described above, the emotion prediction process isrestricted to reduce the load of the emotion prediction process in acase where it is predicted that the emotion prediction process cannot besuitably executed, such as a case where the processing load of the CPUor GPU is high, a case where the user's facial expression cannot beacquired, or a case where it is predicted that the user's motion doesnot reflect his/her emotions. Accordingly, it is possible to preventprocesses of other applications executed by the emotion predictiondevice 100 from being delayed or stopped.

Further, in the embodiment, it is possible to determine whether the useris seated, whether the user's facial expression can be acquired, orwhether the user is operating the device, without depending on the imageprocessing based on the image data acquired from the image sensors, suchas the TOF camera 21 and the IR camera 22, which is generally requiredfor the processing having a high load. Consequently, the determinationas to whether the execution state is to be shifted can be made while theprocessing load is curbed.

As described above, a technology according to one embodiment of thepresent disclosure has been described. However, the state shift processdescribed above may be appropriately modified in the present disclosure,as long as the load of the emotion prediction process can be reduced ina case where the emotion prediction process is difficult or unnecessary.For example, the details of restricting the functions of the firstprediction unit, the second prediction unit, and the third predictionunit are not limited to those described in the embodiment. Further, forexample, in the embodiment described above, it is assumed that the useris seated in the driver's seat, but a user seated in another seat, suchas the passenger seat, may be a subject of the emotion prediction.

The present disclosure is not limited to the emotion prediction device,but encompasses a method executed by a computer (processor) included inthe emotion prediction device, a program, a computer-readablenon-transitory recording medium storing the program, and a vehicleincluding the emotion prediction device.

The present disclosure is useful for a device that executes emotionprediction.

What is claimed is:
 1. An emotion prediction device mounted on avehicle, the emotion prediction device comprising: a control unitconfigured to execute functions of: a first prediction unit configuredto predict an emotion of a user based on a facial expression of theuser; a second prediction unit configured to predict an emotion of theuser based on a motion of the user; and a third prediction unitconfigured to predict an emotion of the user based on the emotionpredicted by the first prediction unit and the emotion predicted by thesecond prediction unit, wherein the control unit is configured torestrict execution of at least a part of the functions of the firstprediction unit, the second prediction unit, and the third predictionunit, based on at least a processing load of the control unit.
 2. Theemotion prediction device according to claim 1, wherein the restrictionof the execution includes stopping or extending a cycle of executing anyone of the functions of the first prediction unit, the second predictionunit, and the third prediction unit.
 3. The emotion prediction deviceaccording to claim 1, wherein the control unit is configured todetermine a state of the user or the vehicle, and restrict the executionbased on the determination result.
 4. The emotion prediction deviceaccording to claim 3, wherein the control unit is configured to, upondetermining that the user is not seated, stop all the functions of thefirst prediction unit, the second prediction unit, and the thirdprediction unit, as the restriction of the execution.
 5. The emotionprediction device according to claim 3, wherein, the control unit isconfigured to, upon determining that the facial expression of the useris not acquirable, stop the functions of the first prediction unit andthe third prediction unit, as the restriction of the execution.
 6. Theemotion prediction device according to claim 3, wherein the control unitis configured to, upon determining that the user is operating apredetermined device, stop the functions of the second prediction unitand the third prediction unit, as the restriction of the execution. 7.The emotion prediction device according to claim 3, wherein the controlunit is configured not to use any information acquired from one or moreimage sensors provided in the vehicle to determine whether therestriction of the execution is executed.
 8. An emotion predictionmethod executed by a computer of an emotion prediction device mounted ona vehicle, the emotion prediction method comprising: a first predictionstep of predicting an emotion of a user based on a facial expression ofthe user; a second prediction step of predicting an emotion of the userbased on a motion of the user; a third prediction step of predicting anemotion of the user based on the emotion predicted in the firstprediction step and the emotion predicted in the second prediction step;and an execution restriction step of restricting execution of at least apart of the first prediction step, the second prediction step, and thethird prediction step, based on at least a processing load of thecomputer.
 9. An emotion prediction program causing a computer of anemotion prediction device mounted on a vehicle to execute: a firstprediction step of predicting an emotion of a user based on a facialexpression of the user; a second prediction step of predicting anemotion of the user based on a motion of the user; a third predictionstep of predicting an emotion of the user based on the emotion predictedin the first prediction step and the emotion predicted in the secondprediction step; and an execution restriction step of restrictingexecution of at least a part of the first prediction step, the secondprediction step, and the third prediction step, based on at least aprocessing load of the computer.
 10. A vehicle comprising the emotionprediction device according to claim 1.